In a communication system, a terminal may move at a high speed so that a considerable Doppler shift will occur, and the amplitude of a signal may fade rapidly and the phase of the signal may vary rapidly particularly in a multi-path scenario, thus deteriorating the performance of the system. It is thus necessary for a receiving end to adjust adaptively algorithms related to channel estimation and signal detection dependent upon the current moving speed of the terminal, and to this end, an algorithm to measure the speed accurately is required to support such an adaptive adjustment strategy. At present there are the following algorithms to measure the speed:
A. Crossing Rate Algorithm
The crossing rate algorithm is very simple in principle and easy to perform and has been widely applied in real communication systems. The Doppler shift may result in a temporally fluctuating signal so that generally there is a deep fading of the amplitude of the signal once the terminal moves over a distance of half the wavelength. The number of times Le that the level fades per unit time can be counted to thereby estimate the speed. With a carrier frequency fc and the velocity of light c, the speed can be estimated as v=c/fc*Le.
B. Correlation Algorithm
The moving speed may result in the Doppler dispersion of the signal in the frequency-domain, and there is the following relationship between time-domain autocorrelation of the received signal and the Doppler dispersion over a Rayleigh channel:ρx(τ)=σ2J0(2πfmτ)  (1)
Where fm represents the largest Doppler dispersion, τ represents a correlation time, ρx(τ) represents autocorrelation of the signal, σ2 represents noise power, and J0(•) represents a Bessel function of the first kind of order zero with a curve as illustrated in FIG. 1. Thus a statistic of a time-domain autocorrelation value of the signal is made from the time-domain autocorrelation characteristic of the signal, and the Doppler dispersion fm is estimated against a lookup table of Bessel function curves to thereby estimate the moving speed. The equation (1) has to be revised for use in view of a direction of arrival distributed non-uniformly and affected by a Rician factor K over a Rician channel.
A general problem with the crossing rate algorithm is how to count Le accurately. There may be a large number of observable burrs of the signal in time-domain being affected by noise and the channel. The number of times that the level fades can be counted accurately only after the signal is de-noised, de-burred, etc. Moreover the accuracy in estimation of the speed may also be affected by the statistic operation for the level crossing rate. The signal has to be preprocessed in the algorithm nevertheless at low precision.
The count characteristic in the correlation algorithm can only be applicable to the Rayleigh channel but not to the Rician channel, so the algorithm has to be revised by the Rician factor in the other scenarios, but the Rician factor K may not be easy to determine, thus complicating the algorithm. Moreover the Bessel curve is not a monotonic function, and in order to estimate the speed accurately, 2πfmτ<4 shall be guaranteed, and if there is a significant Doppler dispersion fm at a high speed, then the Doppler dispersion can be estimated only if the value of τ is very small, so that the correlation algorithm may be restricted greatly at a high speed. Moreover the statistic operation for autocorrelation may also affect the precision of the algorithm.
Furthermore with both of the methods above, generally after channel estimation is performed on respective received signals, a statistic of derived channel response values throughout the bandwidth has to be made with a considerable effort of calculation, and a current calculation result can only be applicable, at some delay in time, to next channel estimation and channel detection